forecasting-matlab

MATIAB各种经典算法的程序，包括数据分析、绘图等等(MATLAB)

联合开发网 - pudn.com

说明：  对数值进行分析，AP聚类算法是基于数据点间的"信息传递"的一种聚类算法。与k-均值算法或k中心点算法不同，AP算法不需要在运行算法之前确定聚类的个数。AP算法寻找的"examplars"即聚类中心点是数据集合中实际存在的点，作为每类的代表(For numerical analysis, AP clustering algorithm is based on the "information transfer" between data points. Unlike k-means algorithm or k-center algorithm, AP algorithm does not need to determine the number of clusters before running the algorithm. The "examples" searched by AP algorithm is the actual points in the data set as the representative of each class)

一个基于边信息嵌入的数字水印程序,经过本人的实验,已实现,供各位享用(A side information embedded based on digital watermarking process, after my experiments have been realized, for your enjoyment)

THIS PROGRAMME IS A SOLUTION OF AN EXERCICE OF THE BOOK OF ROBOTICS OF CRAIG.

说明：  该程序主要是仿真单极性码和双极性码在加性高斯白噪声信道中的理论误码率和实际误码率，并做一比较(The program is mainly simulation of single polar code and bipolar code theory in additive white gaussian noise channel bit error rate and the actual error rate, and make a comparison)

for master degree seminar good file from ieee site

基于MATLAB开发的PID控制程序，含有详细说明温习，初学者可以参考学习。(MATLAB-based development of the PID control program, with detailed study, beginners can refer to learn.)

有关通信中的多用户检测的matlab的程序代码，有需要的可以下载啊(About communication in multi-user detection matlab code, there is a need to download ah)

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)